First developed a method for determining the optimal architecture of a neural network for solving typical problems of vocal interaction in the information system of distance learning. In determining the optimal architecture used multi-criteria evaluation approach to ensure the possibility of the main requirements of these tasks in the tested neural network architectures. Established a list of optimization criteria: learning on noisy data, the use of correlated training examples need to be displayed in a training set of all aspects of the process, proportionality studies, and recognized classes, the ability to self-learning, the quality of teaching, the ability to automate learningadaptability to uplearning, durations required for training volume of computing resources, memory, generalization error, calculate the duration required to detect the amount of computing resources. The importance of the criteria for the task proposed to estimate using weights that are based on expert data. Proved that to proven network architectures include: multilayer perceptron, radial basis function network, a network of Grossberg, Kohonen map, deep neural networks. For all these architectures are obtained when evaluating the feasibility of each of these criteria. An example of determining the optimal architecture of a neural network for phoneme recognition in the voice user authentication at login.